Generation-Level Parallelism for Evolutionary Computation : A Pipeline-Based Parallel Particle Swarm Optimization

Jian-Yu LI, Zhi-Hui ZHAN, Run-Dong LIU, Chuan WANG, Sam KWONG, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

45 Citations (Scopus)


Due to the population-based and iterative-based characteristics of evolutionary computation (EC) algorithms, parallel techniques have been widely used to speed up the EC algorithms. However, the parallelism usually performs in the population level where multiple populations (or subpopulations) run in parallel or in the individual level where the individuals are distributed to multiple resources. That is, different populations or different individuals can be executed simultaneously to reduce running time. However, the research into generation-level parallelism for EC algorithms has seldom been reported. In this article, we propose a new paradigm of the parallel EC algorithm by making the first attempt to parallelize the algorithm in the generation level. This idea is inspired by the industrial pipeline technique. Specifically, a kind of EC algorithm called local version particle swarm optimization (PSO) is adopted to implement a pipeline-based parallel PSO (PPPSO, i.e., P3SO). Due to the generation-level parallelism in P3SO, when some particles still perform their evolutionary operations in the current generation, some other particles can simultaneously go to the next generation to carry out the new evolutionary operations, or even go to further next generation(s). The experimental results show that the problem-solving ability of P3SO is not affected while the evolutionary speed has been substantially accelerated in a significant fashion. Therefore, generation-level parallelism is possible in EC algorithms and may have significant potential applications in time-consumption optimization problems.
Original languageEnglish
Pages (from-to)4848-4859
JournalIEEE Transactions on Cybernetics
Issue number10
Early online date4 Nov 2020
Publication statusPublished - Oct 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102102; in part by the Outstanding Youth Science Foundation under Grant 61822602; in part by the National Natural Science Foundations of China under Grant 61772207 and Grant 61873097; in part by the Key-Area Research and Development of Guangdong Province under Grant 2020B010166002; in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003; and in part by the Hong Kong GRF-RGC General Research Fund 9042489 under Grant CityU 11206317.


  • Evolutionary computation (EC)
  • parallel
  • particle swarm optimization (PSO)
  • pipeline technique


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